The new “epidemic map” feature introduced by Baidu Maps tracks the daily population flow of more than 300 cities in China, including the departure and destination of passengers, the scale and trend of migration in each city, and travel intensity. Photo: CGTN

The unpredictability of the occurrence and spread of epidemics poses formidable challenges to global health security. Such uncertainty is highly correlated with the complexity of human social behavior. Therefore, epidemic prevention and control should stress not only the biological study of pathogens but also social behavior factors.

In today’s media-oriented society, digital media is an important realm of social communication and daily life, and thus also an important channel for disseminating and acquiring information about the epidemic. The construction of a digital mechanism for major epidemic prevention and control based on algorithms is thus of great practical significance for resolving deficiencies in the current epidemic prevention system and for realizing the transition from an emergency management strategy to an early warning strategy.

Necessity, urgency

The construction of a digital mechanism for major epidemic prevention and control not only marks innovation in how it is that we come to know and understand viruses, but also signals a shift of focus in how we carry out epidemic prevention and control.

According to a survey by the National Academy of Development and Strategy at Renmin University of China, social media will be the main channel for people to obtain information in the 5G era. People learn more information about the world in the form of digital signals, rather than through traditional social investigation and statistical analysis.

Compared to the traditional epidemic prevention system that relies on medical observation, clinical treatment and experts as sources of information, digital epidemic monitoring mainly focuses on unstructured, informal social media and web data, which fundamentally changes the basis and key of the epidemic monitoring system in terms of data collection, information dissemination and communicative intervention.

Early in February, the World Health Organization warned that the COVID-19 outbreak and response has been accompanied by a massive “information epidemic,” or “infodemic” that makes it hard for people to find trustworthy sources and reliable guidance when they need it.

The spread of the disease has been shadowed by the spread of panic, rumors and myths. In prevention and control efforts, it is crucial to control the infection of pathogens and, at the same time, to be wary of the social disorder and secondary disasters caused by widespread false information and malicious rumors.

In this light, the establishment of a digital mechanism for major epidemic prevention and control based on social media and the internet is conducive to the detection of, analysis of and response to infodemics.

In the meantime, the concept of national security might be reexamined and redefined, by highlighting the importance of information security, biological security, public health security and other non-traditional security threats, in order to better cope with a pandemic.

To this end, the significance of a digital epidemic monitoring mechanism based on information mining is two-fold for the maintenance of national security in today’s world. For one, it can help stay on top of the global transmission of the disease, so as to provide early warning information. In addition, the detection and response to the infodemic is conducive to the construction of a new national security system that coordinates national biosafety, public health security and cybersecurity.

Status quo

In recent years, many epidemiological studies across the globe have applied intelligent algorithms, signal tracking and global positioning technology to dig into health data embedded in social media and search engines, in order to predict the timing, scope and type of disease outbreaks and then intervene with targeted prevention measures.

Researchers coined concepts such as “digital surveillance,” “digital epidemiology” and “internet-based biological monitoring” to describe the new model of epidemiological research and surveillance based on the application of network information technology and data modeling analysis.

Digital surveillance platforms, such as HealthMap, ProMED-Mail, and the Global Public Health Intelligence Network, not only monitor information from governmental health agencies, non-governmental organizations, news wires and websites, but also encourage the public report on their own health status in real time. Their goals are to deeply participate in the whole process of epidemic prevention and control, act as a whistleblower for emergent diseases, and ultimately predict epidemics ahead of traditional epidemic prevention agencies.

In the prevention and control of the COVID-19 outbreak in China, tech giants such as AutoNavi and Baidu and a number of media outlets have utilized their data advantage to developed auxiliary tools that can track the activity of confirmed cases, create thermal maps of migrating populations, density of passengers on subways, buses, etc., to provide users with real-time, convenient and intuitive anti-epidemic services.

However, the existing digital surveillance system still has many deficiencies. For starters, these platforms are heavily reliant on an open and transparent information environment. If the flow of epidemic information is not smooth, these platforms will not properly monitor the scenario and project early warnings.

For example, the Google Flu Trend service was shut down in 2015 due to discrepancies between estimates of flu cases and de facto statistics. In addition, “data overload,” “data noise” and epidemic rumors in cyberspace also significantly hampered the normal operation of their data-aggregation tools.

These platforms are limited by the risks of invading personal privacy and even contributing to stigmatization, racial prejudice and other concerns harming infected persons and regions. Therefore, it is necessary to seek an optimal balance and intersection between network data and clinical information, algorithm governance and social participation, personal privacy and public interest.

Concrete measures

Going forward, it is crucial to perfect data mining, cleansing and analysis algorithms, enhance our capacity to model epidemic dynamics, and promote the public’s media literacy and participation, so as to construct an epidemic prevention and control system featuring “government coordination, expert argumentation, public participation, technical support and multi-player interaction.”

On the basis of the existing epidemic information reporting system, it is essential to build an internet-based epidemic information mining and analysis system, featuring a portal for the public to submit information directly, so as to break the barriers between the traditional epidemic information reporting channels and internet data-based methods.

Through the comparison, verification and supplementation of the data collected from both channels, the professionalism, comprehensiveness and accuracy of information reporting of major epidemics in a real-time manner can be realized, to effectively avoid epidemic spillover.

Tech giants such as Alibaba, Tencent, Sina and Baidu are expected to share relevant data resources in accordance with laws and regulations, and they need to participate in the collection, identification and reporting of epidemic information at home and abroad, as well as the intelligent screening and fact checking of false information and rumors.

At the same time, the public shall be better equipped with targeted epidemiology and national biological safety knowledge, in order to enhance their risk, responsibility and participation awareness, improve media literacy, and, in particular, strengthen their ability to protect themselves against an infodemic. Only then can the public get more involved in major epidemic information monitoring, transmission and governance.

It is worth noting that the COVID-19 outbreak highlights the important role of grassroots communities in national governance. Therefore, it is necessary to promote the organic integration of a digital mechanism for major epidemic prevention and control with the social governance of grassroots communities and also to facilitate the interaction between online data and offline governance.

In this aspect, a community-level comprehensive governance database integrating population information, flow trajectories, social relations, emergencies and other factors needs to be set up to carry out real-time checks on the health status of migrant populations to catch potential disease risks and to conduct the timely detection, early warning and decisive handling of epidemic diseases once they occur.

Due to the cross-regional transmission of diseases and infodemics, it is increasingly important to increase the targeted collection and analysis of overseas epidemic information to win time for epidemic prevention and control at home. China should keep strengthening its data sharing, communication and coordination with the WHO and other national and regional health and epidemic prevention agencies.

In view of the infodemic amid the COVID-19 pandemic and potential regional and racial stigmas, we should take advantage of international communication resources to share the story of China’s fight against the disease.

Zhao Libing is from the School of Journalism and Communication at Southwest University of Politics and Law.